CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images.
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| Title: | CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images. |
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| Authors: | Wang, Yuebao1 (AUTHOR), Yang, Guang1,2 (AUTHOR) yangguang@cidp.edu.cn, Guo, Xiaotong1 (AUTHOR), Lu, Wangze1,2 (AUTHOR), Liu, Rongxiang1 (AUTHOR), Huang, Meng1,2 (AUTHOR), Liu, Shuai1,2 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1198. 24p. |
| Subjects: | Optical remote sensing, Remote sensing, Debris avalanches, Landslides, Emergency communication systems, Landslide hazard analysis |
| Abstract: | Highlights: What are the main findings? We constructed the Composite Geological Hazards Dataset (CGHD), a large-scale, multi-scale and multi-resolution dual-temporal dataset integrating both landslides and debris flows from diverse optical satellite sources. Experimental results demonstrate that the proposed use of dual-temporal and multi-source optical remote sensing data in CGHD significantly improves detection accuracy and enhances generalization across diverse geographic environments. What are the implications of the main findings? CGHD establishes a solid data foundation for landslide and debris flows hazard research, enabling models to effectively learn temporal dynamics and adapt to varying spatial resolutions and sensor characteristics in complex terrains. This resource is pivotal for advancing intelligent disaster monitoring and prevention, facilitating the development of reliable automated systems for rapid landslides and debris flows mapping and emergency response. Geological hazards are characterized by their sudden occurrence, high destructiveness, and wide spatial impact. In particular, landslides and debris flows triggered by earthquakes and intense rainfall often lead to severe casualties and substantial property losses. Therefore, the rapid delineation of affected areas is crucial for disaster assessment and post-disaster reconstruction. To this end, several geohazard datasets have been developed from remote sensing imagery, focusing on specific regions, disaster types, and data sources, providing valuable support for geohazard detection and risk assessment. Our study addresses the diversity of real-world geological disasters in terms of their types, causes, and spatial distribution and constructs the Composite Geological Hazards Dataset (CGHD), a dual-temporal geohazard dataset that enhances generalisation and practical applicability. CGHD incorporates pre- and post-disaster remote sensing images of 14 landslide and debris flow events that occurred worldwide between 2017 and 2024, collected using four remote sensing platforms and encompassing multiple spatial scales and land-cover categories. The affected areas varied significantly in size and shape, with land-cover types including roads, buildings, vegetation, farmland, and water bodies. This resulted in 3963 pairs of pre- and post-disaster images, each with a size of 1024 × 1024 pixels. We validated the reliability of the CGHD through experiments with nine change-detection models and further evaluated its generalisation capability using an unseen dataset. The experimental results demonstrate that CGHD achieves high recognition accuracy and strong generalisation across diverse geographic environments, providing comprehensive data support for intelligent geohazard recognition and disaster assessment. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We constructed the Composite Geological Hazards Dataset (CGHD), a large-scale, multi-scale and multi-resolution dual-temporal dataset integrating both landslides and debris flows from diverse optical satellite sources. Experimental results demonstrate that the proposed use of dual-temporal and multi-source optical remote sensing data in CGHD significantly improves detection accuracy and enhances generalization across diverse geographic environments. What are the implications of the main findings? CGHD establishes a solid data foundation for landslide and debris flows hazard research, enabling models to effectively learn temporal dynamics and adapt to varying spatial resolutions and sensor characteristics in complex terrains. This resource is pivotal for advancing intelligent disaster monitoring and prevention, facilitating the development of reliable automated systems for rapid landslides and debris flows mapping and emergency response. Geological hazards are characterized by their sudden occurrence, high destructiveness, and wide spatial impact. In particular, landslides and debris flows triggered by earthquakes and intense rainfall often lead to severe casualties and substantial property losses. Therefore, the rapid delineation of affected areas is crucial for disaster assessment and post-disaster reconstruction. To this end, several geohazard datasets have been developed from remote sensing imagery, focusing on specific regions, disaster types, and data sources, providing valuable support for geohazard detection and risk assessment. Our study addresses the diversity of real-world geological disasters in terms of their types, causes, and spatial distribution and constructs the Composite Geological Hazards Dataset (CGHD), a dual-temporal geohazard dataset that enhances generalisation and practical applicability. CGHD incorporates pre- and post-disaster remote sensing images of 14 landslide and debris flow events that occurred worldwide between 2017 and 2024, collected using four remote sensing platforms and encompassing multiple spatial scales and land-cover categories. The affected areas varied significantly in size and shape, with land-cover types including roads, buildings, vegetation, farmland, and water bodies. This resulted in 3963 pairs of pre- and post-disaster images, each with a size of 1024 × 1024 pixels. We validated the reliability of the CGHD through experiments with nine change-detection models and further evaluated its generalisation capability using an unseen dataset. The experimental results demonstrate that CGHD achieves high recognition accuracy and strong generalisation across diverse geographic environments, providing comprehensive data support for intelligent geohazard recognition and disaster assessment. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081198 |
